INTERNATIONAL JOURNAL OF ELECTRONICS AND   International Journal of Electronics and Communication Engineering & Technology...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 097...
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Hybrid method to compress slices of 3 d medical images

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Hybrid method to compress slices of 3 d medical images

  1. 1. INTERNATIONAL JOURNAL OF ELECTRONICS AND International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEMECOMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)ISSN 0976 – 6464(Print)ISSN 0976 – 6472(Online)Volume 4, Issue 2, March – April, 2013, pp. 250-256 IJECET© IAEME: www.iaeme.com/ijecet.aspJournal Impact Factor (2013): 5.8896 (Calculated by GISI) ©IAEMEwww.jifactor.com HYBRID METHOD TO COMPRESS SLICES OF 3D MEDICAL IMAGES Mayuri Y. Thorat1 and Vinayak K. Bairagi2 Electronics and Telecommunication Dept, Sinhgad Academy of Engineering, Kondhwa (Bk), Pune-411048, Maharashtra, India ABSTRACT Now days 3D medical images like MRI, CT are integral part of standard health care. These images are rich in volume and provide important diagnostic information, so there should be some proper method to compress these images. The method proposed in this paper is symmetry based technique for lossless compression of 3D medical image data. The proposed method uses anatomic symmetries present in structures of medical images to reduce energy of sub-bands. It uses Run Length Encoder and Huffman coder, which encodes the residual data generated after prediction to provide resolution and quality scalability. The technique can be compared with other compression techniques like RLE and Huffman. It gives an average improvement in compression ratios. Keywords: symmetry; volumetric images; lossless compression; RLE; Huffman. 1. INTRODUCTION Telemedicine is the use of electronic information to communicate technologies to provide and support healthcare when distance separates the participants. It needs the fast and error free communication of medical images to their destination to perform e-consultancy between various specialists to agree upon the correct diagnosis of patient [1]. As Telemedicine deals with diagnosis of medical data there is no scope of information loss and delay in transmission. If we are dealing with medical images they require large storage space, and large transmission time [2]. In recent years 3D medical images like Magnetic Resonance Imaging (MRI), Computed Tomography (CT) are considered as important part of standard health care. As the amount of 3D medical images generated increases, the storage, management, and access to these large repositories is becoming increasingly complex. Because this data provides 250
  2. 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEMEimportant diagnostic information, care must be taken in compressing it. Compression methodsare classified into lossless and lossy methods. In the medical image compression lossyschemes are generally not used to avoid possible loss of useful clinical information whichmay influence diagnosis [3]. As 3D medical images are large in volume and consist ofvaluable data, lossless compression is usually the standard in medical imaging to avoid anynegative effects on image quality and diagnostic capabilities. The most desirable properties ofany compression method for 3D medical images include: high lossless compression ratios,resolution scalability, and quality scalability. All of we know that human body has verticalsymmetry, which means one half part of body is approximate replica of other half part. In thispaper we are going to make use of this symmetry of human anatomy to achieve maximumcompression of medical images without any losses. There are some examples of symmetrichuman body organs as axial view of brain, pupil, labia, cervical, lumber, chest, thorax, larynx,lungs, etc. In this paper, we propose a scalable lossless compression method for 3D medicalimages that attains the three desired properties listed above and uses the symmetricalcharacteristics of the data to achieve a higher lossless compression ratio. The method is welldescribed in further discussion.2. BLOCK DIAGRAM Original Check whether Image Symmetry Left Part – Medical Image Shifted or Rotated Detection Right Part Compressed Encoding of Data Stream Residual Data Figure 1: Block diagram of proposed method The input images are 3D medical images which are output of MRI of different humanbody organs. These images are having axial symmetry. The very first step is to checkwhether the input image is appropriately positioned. That means we have to check that imageis neither rotated nor shifted. If this is so then first step is to convert it in proper form. Nextblock performs symmetry detection. After finding axis of symmetry the image is divided into two parts as left part and right part. These two parts are separated and subtracted from eachother to get residual data. This residual data is encoded using RLE coding and Huffmancoding to get compressed bit stream.2.2.DETECTING AXIS OF SYMMETRY Detecting axis of symmetry is an important issue. Several techniques have beenproposed for detecting symmetry. One of simple and efficient of them is by finding thecentroid of the image. We have first found out the centroid and the edges of the images usingdifferent MATLAB functions. After finding the axis of symmetry of the image, we assumethat the image is almost symmetrical. The assumption is based on the fact that we are mainlyusing human medical images and that the human body is said to be largely symmetrical.2.3. RESIDUAL DATA Due to the inherent symmetry of the human anatomy, cross-sections of the ROIsdepicted in slices of 3D medical images are typically symmetrical [5]. There are two types of 251
  3. 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEMEsymmetries, global symmetry and local symmetry. Global symmetry refers to the symmetryof the whole sub-band that is nothing but a main axis of symmetry, while local symmetry refersto the symmetry of a small region within the sub-band as defined by a local axis of symmetry [6].Here in this method we have used only the concept of Global symmetry to avoid complexness oflocal symmetry. After getting centroid of the image next step is to plot axis of symmetry. Along the axisof symmetry the image is divided into two parts. By doing this we can partition image in to twoareas of equal size as left part (LHi-L) and right part (LHi-R) [1]. We calculated the differencebetween the left part and right part that generates the residual data with less energy than theoriginal data. So we can consider following example of axial view of brain [1]. The figure 4 shownbelow gives high pass sub-band (LH) of an MRI slice. It is easy to find out main vertical axis ofsymmetry which is centred in the sub-band. So we can make partition of sub-band into areasalong with axis of symmetry. Figure 4: Horizontal high pass sub-band of slice of an MRI volume of the axial view of human headLet’s denote area to the left of the axis as LHi-L and the area to the right as LHi-R. If LHi-L is tobe flipped along the axis of symmetry, it would be expected to provide a good approximation toLHi-R and can therefore be used to predict LHi-R. So this data i.e. residual data obtained fromright part and flipped left part and complete right part is used as input to the encoder.2.4. ENCODING RESIDUAL DATA Run-length encoding (RLE) is a very simple form of data compression in which runs ofdata are stored as a single data value and count, rather than as the original run [12]. RLE worksby reducing the physical size of a repeating string of characters. The main advantage of RLE isthat, it performs lossless compression of data. The other encoder we are using is Huffmanencoder. Huffman codes are variable-length codes and are optimum for a source with a givenprobability model. In Huffman coding, more probable symbols are assigned shorter codewordsand less probable symbols are assigned longer codewords to find the code.So at the output of this block we are getting data which is compressed bit stream. Now this datacan be easily stored and transmitted. 252
  4. 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME2.5.RECONSTRUCTION OF ORIGINAL IMAGE. The output of the encoder is compressed bit stream which is to be reconstructed at thedecoder. At the receiver side we have to reconstruct image by following inverse of thealgorithm which is used for encoding. So we have to first decode the compressed bit streamby run length decoder or Huffman decoder whichever is used to encode the data. This data isthen converted to image. The last step is to add the residual part in that image which will givethe image same as the input image. Hence the technique is completely lossless.3. RESULTSPart I:The results shown in table1 are achieved by performing simple subtraction of 3D medicalimages. Table 1: Compression achieved by performing simple multiplication Original Image Subtracted Image Size of Size of Name Entropy Size (kb) Difference Entropy Size (kb) image after decoded RLE imageA83GL6 5.2828 530 1 5.1701 178 154 178 G0 2D 178A83GL6 5.4097 652 1-2 1.9645 94 90 218 G2 2D 218A83GL6 5.3654 652 2-3 1.7083 82 65 219 G4 2D 219A83GL6 5.4397 647 3-4 2.7519 138 129 217 G6 2D 217A83GL6 5.3867 620 4-5 3.1915 157 149 208 G8 2D 208First of all we convert 3D image into 2D image. The first image is as the reference forreconstruction so it is stored as it is. Second image is now subtracted from first image andonly residual part is stored as second image instead of complete second image. Then thirdimage is subtracted from second image and only residual part is stored as third image justlike did for second image. And hence so on. So by doing this we can achieve goodcompression. But this method results in some minor losses which we cannot afford whileworking with medical images. The same idea we used in our algorithm by making use ofanatomic symmetry.Part II: The results shown in the table 2 gives comparison between the RLE, Huffman andproposed method. Eight data sets of MRI images of different body organs are taken. Theimages are compressed first by the proposed method of symmetry and then compression ratiois calculated. Then images are compressed by only RLE and also by Huffman, againcompression ratio is calculated. The table shows that our proposed method gives good resultsover RLE and Huffman both. 253
  5. 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME Table 2: Comparison of compression ratios of MRI Images from database Original Compression Ratio Image Only RLE RLE + Symmetry Only Huffman Huffman + Symmetry(a) Brain 3.95:1 4.5:1 2.87:1 3.37:1(b) Brain 4.47:1 5.43:1 2.56:1 3.54:1(c) L Spine General 2.96:1 3.63:1 2.07:1 2.3:1(d) L Spine 5.52:1 6.37:1 3.68:1 4.47:1(e) C Spine General 1.52:1 1.84:1 1.18:1 1.32:1(f) T Spine General 3.34:1 4.40:1 2.62:1 3.01:1(g)Pelvis & Hip 2.20:1 3.11:1 1.92:1 2.23:1General(h) Abdomen 3.36:1 4.56:1 2.55:1 3.35:1The input images shown in figure 5 to which the above coding algorithm is applied for togenerate compressed bit stream. Then decompression algorithm is applied to get the originalimage back from the compressed data, which is shown in the figure 6. The output image is thedecompressed image i.e. from the figure 6 it is clear that the output image of the algorithm isexactly same as the input image. So proposed method is exactly lossless. Image 1 Image 2 Figure 5: Original Medical Images Image 1 Image 2 Figure 6: Reconstructed Images obtained after applying proposed algorithm 254
  6. 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME4. FUTURE WORK In this paper we have got comparative results for RLE, Huffman and proposedmethod. Our next approach is to use several different encoding techniques to compress andreconstruct medical images without any loss. The comparative study will help to choose bettermethod. We will try to improve compression ratio more.5. CONCLUSION In this paper, an image compression algorithm that utilizes anatomic symmetriespresent in medical images is proposed. The algorithm first divides image slice into two partsalong the axis of symmetry. The residual part is found out by comparing two parts, which is tobe encoded using Huffman or RLE. This gives compressed bit stream at the output of encoderwhich is decoded at receiver to reconstruct the image. This paper focused on the evaluation of several commonly used algorithms for losslesscompression. The proposed method is a 3D scalable lossless compression of medical imagedata. So our aim is to increase compression ratio with reducing complexity while achievingcompression.6. REFERENCES[1] V. Sanchez, R. Abugharbieh,and P. Nasiopoulos , (July 2009), “Symmetry based scalable lossless compression of 3D medical image data”, IEEE transaction of medical imaging, vol. 28 no.7, pp 1062-1072.[2] Amrita Pal, Victor W.A, A. Mbarika, Fay Cobb-Payton, Pratim Datta and ScottMcCoy, (March 2005) “Telemedicine Diffusion in a Developing Country: The Case of India” IEEE Transactions of Information Technology in Biomedicine, Vol.9, No.1.[3] R. Sumalatha, M.V. Subramanyam, (August 2010) “Region based coding of 3D Magnetic Resonating Imaging for telemedicine applications”, International Journal of computer applications, Vol. 5, No. 12, pp 1-3.[4] K. K. M. Marcellin, A. Bilgin, and M. Nadar, (Sep 2006), “Efficient transmission of compressed data for remote volume visualization,” IEEE Transaction of medical imaging, vol.25, No. 9, pp 1189-1199.[5] M. Firoozbakht, J. Dehmeshki, M. Martini, (2010) Y. Ebrahimdoost, H. Amin, M. Dehkordi, A. Youannic, SD. Qanadli “Compression of digital medical images based on multiple regions of interest”, IEEE 2010 Fourth International Conference on Digital Society.[6] V. Sanchez, R. Abugharbieh,and P. Nasiopoulos, (Sep 2009), “3D Scalable lossless compression of medical images based on global and local symmetries”, IEEE transaction of medical imaging pp 2525-2529.[7] Prof. S.K. Mishra, Sh. DeepakGupta, Dr Jagdish Kaur, (2007) “Telemedicine in India : Initiatives and vision” IEEE transaction of image compression,vol 4, pp 1-3.[8] V Naga Pruthvi Raj and Dr T Venkateswarlu, (Nov 2007) “A Novel Approch To Medical Image compression using sequential 3D DCT”, IEEE transaction of image compression, vol 11, pp 146-149.[9] Mario Mustra, Kresimir Dolac, Mislav Grgic, (Sep 2008) “Overview of DICOM standard”, 50th International Symposium ELIMAR-2008, pp 39-44. 255
  7. 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME[10] Mattew J. Zukoski, Terrance Boult, Tunc Lyriboz (2006) “A novel approach to medical image compression” International journal of Bioinformatics research and applications, Vol. 2, No.1, pp 89-103.[11] Schelkens, PMunteanu, A.Barbarien, J.Galca, M.Giro-Nieto, X. Cornelis.J., (March 2003) “Wavelet coding of volumetric image datasets” IEEE Transaction of Medical imaging, vol.22,no.3,pp.441-458.[12] K. Thyagarajan, (2011) “Still image and video compression with Matlab”, John Wiley and sons INC publication 2011 edition.[13] R. Edbert Rajan and Dr.K.Prasadh, “Spatial and Hierarchical Feature Extraction Based on Sift for Medical Images”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 308 - 322, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.[14] Rohini N. Shrikhande and Vinayak K. Bairagi, “Prediction Based Lossless Medical Image Compression”, International journal of Electronics and Communication Engineering &Technology (IJECET), Volume 4, Issue 2, 2012, pp. 191 - 197, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472.[15] P. Prasanth Babu, L.Rangaiah and D.Maruthi Kumar, “Comparison and Improvement of Image Compression using Dct, Dwt & Huffman Encoding Techniques”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 1, 2013, pp. 54 - 60, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 256

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